Prior work [1]}, [2]} has demonstrated that few-shot classifiers can benefit greatly from non-episodic pre-training. For traditional metric learning based approaches, the feature extractor is initially trained as a linear classifier with global average-pooling on the full set of training classes. The linear layer is subsequently discarded, and the feature extractor is fine-tuned episodically.
